ArticleJournal of intensive care2026
Development of a deep-learning model for classification of intensivists' extubation decisions using ventilator graphic monitor images: a prospective observational study.
Article in Journal of intensive care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundTimely extubation requires advanced expertise and extensive clinical experience; however, intensive care specialists are scarce globally, and interpreting ventilator graphic monitor waveforms can be challenging for less experienced healthcare providers. Given these challenges, we aimed to develop a deep-learning model to classify extubation decisions by intensivists based on ventilator graphic monitor images obtained at the time of the spontaneous breathing trial (SBT) outcome determination.
methodsThis single-center prospective observational study was conducted in the Intensive Care Unit of Yamagata University Hospital between August 2023 and July 2025. Adult patients aged ≥ 20 years who received mechanical ventilation and underwent SAT and SBT using a Puritan Bennett
resultsA total of 191 patients were included in the final analysis: 160 (83.8%) in the extubated group and 31 (16.2%) in the non-extubated group. MobileNetV3-Large demonstrated the highest performance, with an area under the curve (AUC) of 0.900 (95% confidence interval 0.807-0.993), sensitivity of 0.900, specificity of 0.727, and accuracy of 0.759. All four architectures exhibited consistently high performance (AUC: 0.827-0.900). The Grad-CAM analysis suggested that the model tended to focus on the entire waveform, including pressure, flow, and volume, in the extubated group while focusing primarily on the flow waveform in the non-extubated group.
conclusionsThis study developed a deep-learning model capable of classifying intensivists' extubation decisions from ventilator graphic monitor images with good classification performance (AUC 0.900). As a proof of concept, this approach suggests that waveform-based components of intensivists' decision-making may be approximated from image data, although further validation with larger multicenter datasets is required.
trial registrationThe study was registered in the UMIN Clinical Trials Registry (UMIN000051938).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.